Open Access Open Access  Restricted Access Subscription Access

Brain Tumor Detection from MRI Using Deep Learning and Genetic Algorithm

Gadha M M, Akshay Raj K R, Devadath Sudhir, Hemand K P, Neeraj K R

Abstract


This paper explores the methodology for the implementation of a brain tumor detection tool that uses the advantages of the deep learning strategy and also, in addition, the genetic algorithm is used for the optimization of the detection model. The model works by taking a large amount of brain MRI images and perform training using both the CNN and the Genetic algorithm to predict the results. There are several pre-processing steps are done on the MRI input images. Optimizing using the genetic algorithm makes the model more accurate in predictions. It also helps the healthcare organizations to aware about the brain tumors and more fast and reliable they can figure out. The strategy used can extensively enhance the general overall performance of the brain tumor detection from the MRI images and offering a scalable and adaptable solution that may be carried out in the real time. The model can also be used for certain other medical conditions.


Full Text:

PDF

References


Amin Kabir Anaraki, Moosa Ayati and Foad Kazemi, “Magnetic reso-nance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithm,” BioCybernetics and Biomedical Engineering, vol. 39, pp. 63–74, 2019.

Kiruthika Lakshmi V, C. Amarsingh Feroz and Asha Jenia Merlin J, “Automated detection and segmentation of brain tumor using genetic algorithm,” in Proc. Int. Conf. Smart Syst. Invent. Technol. (ICSSIT), IEEE, vol. CFP18P17-ART, pp. 583–589, 2018.

S. Ahmad and P. K. Choudhury, ”On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images,” IEEE Access, vol. 10, pp. 59099–59114, June 2022.

L. Zeinalkhani, A. Ali Jamaat, and K. Rostami, “Diagnosis of Brain Tumor Using Combination of K-Means Clustering and Genetic Algo-rithm,” Iran J. Med. Inform., vol. 7, no. 1, pp. e6, Nov. 2018.

Sarah Zuhair Kurdi, Mohammed Hasan Ali, Mustafa Musa Jaber, Tanzila Saba, Amjad Rehman, and Robertas Damasevievicius,ˇ “Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks,” J. Pers. Med., vol. 13, p. 181, Jan. 2023.

Sakshi Ahuja, B.K. Panigrahi, Tapan Gandhi, and Utkarsh Gautam, “Deep learning-based computer-aided diagnosis tool for brain tumor classification,” in Proc. 11th Int. Conf. Cloud Computing, Data Science and Engineering (Confluence), pp. 854–859, January 2021.

Ahmed kharrat, Karim Gasmi, Ben Messaoud, Nacera Benamrane and Mohamed Abid, “A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine,” Leonardo J. Sci., vol. 17, pp. 71–82, July 2010.

Ahmed H.Abdel-Gawad, Lobna A.Said and Ahmed G.Radwan, ”Op-timized Edge Detection Technique for Brain Tumor Detection in MR Images”, June 2020.

Hanaa ZainEldin, Samah A. Gamel, El-Sayed M. El-Kenawy, Amal H. Alharbi, Doaa Sami Khafaga, Abdelhameed Ibrahim, and Fatma M. Talaat, “Brain Tumor Detection and Classification Using Deep Learning and Sine-Cosine Fitness Grey Wolf Optimization,” Bioengineering, vol. 10, p. 18, 2023.

Sathies Kumar Thangarajan and Arun Chokkalingam, “Integration of optimized neural network and convolutional neural network for auto-mated brain tumor detection,” Sensor Review, vol. 41, no. 1, pp. 16–34, 2021.


Refbacks

  • There are currently no refbacks.